Myeloid cells, including microglia and perivascular macrophages, are central to Alzheimer's disease (AD) neurobiology, yet their role remains incompletely understood. We profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 donors spanning the lifespan and showing varying degrees of AD neuropathology. We delineated six subclasses comprising 13 transcriptionally distinct subtypes and identified adaptive changes associated with aging and AD progression. Here we show that a disease-associated microglial subtype, characterized by elevated GPNMB expression and enriched for polygenic AD risk, expands with AD pathology and shows increased phagocytic activity. We identify MITF as an upstream regulator required to maintain this microglial state. Cell-cell interaction analyses prioritize APOE-SORL1 and APOE-TREM2 signaling pairs associated with disease progression. Using human and mouse models, we demonstrate that the neuroprotective effects of this microglial subtype depend on TREM2. These findings provide mechanistic insights into myeloid cell function in aging and AD, aiding therapeutic discovery.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Genetic risk variants for common diseases are predominantly located in non-coding regulatory regions and modulate gene expression. Although bulk tissue studies have elucidated shared mechanisms of regulatory and disease-associated genetics, the cellular specificity of these mechanisms remains largely unexplored. This study presents a comprehensive single-nucleus multi-ancestry atlas of genetic regulation of gene expression in the human prefrontal cortex, comprising 5.6 million nuclei from 1,384 donors of diverse ancestries. Through multi-resolution analyses spanning eight major cell classes and 27 subclasses, we identify genetic regulation for 14,258 genes, with 857 showing cell type-specific regulatory effects at the class level and 981 at the subclass level. Colocalization of genetic variants associated with gene regulation and disease traits uncovers novel cell type-specific genes implicated in Alzheimer's disease, schizophrenia, and other disorders, which were not detectable in bulk tissue analyses. Analysis of dynamic genetic regulation at the single nucleus level identifies 2,073 genes with regulatory effects that vary across developmental trajectories, inferred from a broad age range of donors. We also uncover 1,655 genes with trans-regulatory effects, revealing distal regulation of gene expression. This high-resolution atlas provides unprecedented insight into the cell type-specific regulatory architecture of the human brain, and offers novel mechanistic targets for understanding the genetic basis of neuropsychiatric and neurodegenerative diseases.

Neurodegenerative and neuropsychiatric diseases impose a significant societal and public health burden. However, our understanding of the molecular mechanisms underlying these highly complex conditions remains limited. To gain deeper insights into the etiology of different brain diseases, we used specimens from 1,494 unique donors to generate a population-scale single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex (DLPFC), comprising over 6.3 million individual nuclei. The cohort includes neurotypical controls as well as donors affected by eight common and complex brain disorders: Alzheimer's disease (AD), diffuse Lewy body disease (DLBD), vascular dementia (Vas), Parkinson's disease (PD), tauopathy, frontotemporal dementia, schizophrenia, and bipolar disorder. We show that inter-individual variation accounts for a substantial portion of gene expression variation in the DLPFC. By comparing transcriptomic variation across diseases, we reveal universal signatures enriched in basic cellular functions such as mRNA splicing and protein localization. After discounting these cross-disease signatures, we show strong genetic and transcriptomic concordance among AD, DLBD, Vas, and PD, largely driven by alteration of synaptic signaling functions in neurons. Furthermore, we characterize transcriptomic variation among different AD phenotypes that were distinct from healthy aging. We uncover mitigating effects of interneurons and aggravating effects of immune and vascular cells in AD dementia. Further exploring the effect of the neuropsychiatric symptoms frequently accompanying AD, we identify a link to deep layer excitatory neurons. By constructing transcriptome trajectories that capture AD progression, we show cell-type specific responses implicated in early and late stages of AD. Our atlas provides an unprecedented perspective of the transcriptomic landscape in neurodegenerative and neuropsychiatric diseases, shedding light on shared and distinct processes involving the neuro-immune-vascular systems, and identifying potential targets for therapeutic intervention.

The dorsolateral prefrontal cortex is central to higher cognitive functions and is particularly vulnerable to age-related decline. To advance our understanding of the molecular mechanisms underlying brain development, maturation, and aging, we constructed a detailed single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex, encompassing over 1.3 million nuclei from 284 postmortem samples spanning the full human lifespan (0-97 years). This atlas reveals distinct phases of transcriptomic activity: a dynamic developmental period, stabilization during midlife, and subtle yet coordinated changes in late adulthood. Modeling non-linear age trends across the lifespan shows ten distinct trajectories of the entire transcriptome from all cell types, with notable findings in neurons and microglia, linked to neurodevelopmental disorders and Alzheimer's disease risk, respectively. Moreover, excitatory neurons exhibit a convergence of gene expression patterns across the lifespan, suggesting the emergence of a common molecular signature of aging. Pseudotime analysis tracing the progression of cellular lineages throughout life reveals key gene clusters with dynamic expression changes that reflect development, maturation, and aging, as well as their connection to brain-related diseases. We uncover significant circadian rhythm reprogramming in late adulthood, characterized by disruption of core clock gene rhythmicity and the emergence of new rhythmic patterns, particularly within microglia and oligodendrocytes. This comprehensive single-cell atlas provides a baseline for understanding the molecular transitions from development through successful aging in the human dorsolateral prefrontal cortex. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We acknowledge the generous support of the National Institute on Aging, who provided funding for this research through the following NIH grants: R01AG067025, R01AG082185, and R01AG065582. Human tissues were obtained from the NIH NeuroBioBank at the Mount Sinai Brain Bank (MSSM; supported by NIMH-75N95019C00049), and NIMH-IRP Human Brain Collection Core (HBCC, project # ZIC MH002903). The results published here are in whole or in part based on data obtained from the AD Knowledge Portal. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Mount Sinai gave ethical approval for this work. Ethics committee/IRB of National Institute of Mental Health Human Brain Collection Core gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data can be accessed via Synapse, as part of the PsychAD Study.The data are available under controlled use conditions set by human privacy regulations. To access the data, a data use agreement is needed.

Neuropsychiatric and neurodegenerative disorders exhibit cell–type–specific characteristics 1–8, yet most transcriptome–wide association studies have been constrained by the use of homogenate brain tissue9–11, limiting their resolution and power. Here, we present a single–nucleus transcriptome–wide association study (snTWAS) leveraging single–nucleus RNA sequencing of over 6 million nuclei from the dorsolateral prefrontal cortex of 1,494 donors across three ancestries–European, African, and Admixed American. We constructed ancestry–specific single–nucleus–derived transcriptomic imputation models (snTIMs) including up to 27 non–overlapping cellular populations, enhancing the resolution of genetically regulated gene expression (GReX) in the brain and uncovering novel gene–trait associations across 12 neuropsychiatric and neurodegenerative traits. Our snTWAS framework revealed cell–type–specific dysregulation of GReX, identifying over 4,000 novel gene–trait associations not detected by bulk tissue approaches. By applying these snTIMs to the Million Veteran Program, we validated major findings and explored the pleiotropy of cell–type–specific GReX, revealing cross–ancestry concordance and fine–mapping causal genes. This approach enhances the discovery of biologically relevant pathways and gene targets, highlighting the importance of cell–type resolution and ancestry–specific models in understanding the genetic architecture of complex brain disorders. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award I01BX004189. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We thank the participants of the Million Veteran Program, the scientists, clinicians and supportive staff involved in the construction of this biobank, and the scientific computing staff for the expertise that they provided. We thank the computational resources and staff expertise provided by the Scientific Computing at the Icahn School of Medicine at Mount Sinai. This study was also supported by the National Institutes of Health (NIH), Bethesda, MD under award numbers R01AG067025 (PR), R01AG082185 (PR), K08MH122911 (GV), R01AG078657 (GV), BX004189 (PR), R01AG065582 (PR), R01AG067025 (PR), R01MH125246 (PR) and T32MH087004 (KT). Human tissues were obtained from the NIH NeuroBioBank at the Mount Sinai Brain Bank (MSSM; supported by NIMH-75N95019C00049), the Rush Alzheimer's Disease Center (RADC; funding: P30AG10161, P30AG72975, R01AG15819, R01AG17917, R01AG22018, U01AG46152, and U01AG61356), and NIMH-IRP Human Brain Collection Core (HBCC, project # ZIC MH002903). This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Award (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the VA Central Institutional Review Board (IRB), and participating studies received approval from their respective IRBs. All participants provided written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All results are included either in the main text or provided in supplementary tables or data.

Parkinson's Disease (PD) is a debilitating neurodegenerative disorder, characterized by motor and cognitive impairments, that affects >1% of the population over the age of 60. The pathogenesis of PD is complex and remains largely unknown. Due to the cellular heterogeneity of the human brain and changes in cell type composition with disease progression, this complexity cannot be fully captured with bulk tissue studies. To address this, we generated single-nucleus RNA sequencing and whole-genome sequencing data from 100 postmortem cases and controls, carefully selected to represent the entire spectrum of PD neuropathological severity and diverse clinical symptoms. The single nucleus data were generated from five brain regions, capturing the subcortical and cortical spread of PD pathology. Rigorous preprocessing and quality control were applied to ensure data reliability. Committed to collaborative research and open science, this dataset is available on the AMP PD Knowledge Platform, offering researchers a valuable tool to explore the molecular bases of PD and accelerate advances in understanding and treating the disease.

Alzheimer’s disease (AD) and Parkinson’s disease (PD) have overlapping characteristics in terms of symptoms and brain pathology. Understanding the similarities in these diseases could lead to more effective treatments. While there is only limited evidence for genetic overlap between the two, enrichment of heritability in genetic regions that are related to microglia has been found in both diseases. However, most of the transcriptomics studies published on AD and PD employed postmortem brains, which obscures the identification of alteration of gene expression within individual cell types. Here we used single-nucleus transcriptomics to examine over eight million nuclei generated from prefrontal cortex samples of over a thousand postmortem donors with AD, PD, both diseases and controls. Then, we built gene regulatory networks (GRNs) to detect the shared gene expression alterations in all major cell types and detected key drivers within each module. Examining multiple levels of transcriptomic organization, such as gene expression and GRNs for both protein-coding and noncoding genes, provided a comprehensive understanding of the molecular pathology of AD and PD. Our analysis revealed cell-specific modules linked to shared and distinct mechanisms of both diseases. Network-derived signatures showed cell-type specificity, implicating regulatory regions within AD and PD genetic risk loci that participate in a variety of biological pathways as well as changes in TF regulation. The integration of transcriptomic and genetic data from AD and PD provides a quantitative, genome-wide resource for mechanistic insight and potential therapeutic development. Funding : U01NS125580, R01AG067025, AARF-21-722200

Aging leads to physiological deterioration and structural changes in the human brain that might lead to cognitive decline. Also, aging in the brain has been associated with a decrease in the number of neurons and synapses. Additionally, the risk of developing neurodegenerative diseases such as Alzheimer’s and Parkinson’s increases with age. This close relationship between aging and neurodegenerative diseases suggests the existence of common molecular mechanisms that regulate gene expression in specific cell types in the brain. It has been challenging to identify these cell specific changes using current gene expression data, as it tends to be confounded by cellular heterogeneity. Here we generate and analyze single-nucleus RNA libraries from prefrontal cortex of n = 269 postmortem controls of age ranging from 2.5-100 years from various ethnic groups (n = 153 Caucasian, n = 93 Afro-American, n = 19 Hispanic, n = 4 Asian). Based on age, we stratified the samples into 5 stages ranging from 1) 2.5-12 years, 2)13-39 years, 3) 40-59 years, 4) 60-80 years and 5) >80 years to compare the transcriptomic changes. We compared changes in gene expression in different cell types across 5 stages and identified genes that changed as a function of age. We also looked at sex-specific transcriptomic changes across these stages. After that, we used age specific genes to predict the biological age of samples across all cell types. Finally, we used a method called WGCNA (weighted gene co-expression network analysis) to investigate specific molecular mechanisms that are unique to each stage. Our study provides the comprehensive view of changes in specific cell types that underlies the aging process. Also, provides a cell-specific list of biomarkers for every stage that can predict normal age that has no effect of disease and is independent of chronological age.

Abstract Motivation In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. Results We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers—GATK and Strelka2—executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System). In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. Availability and implementation SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. Supplementary information Supplementary data are available at Bioinformatics online.

We demonstrate a novel variant calling strategy using barcode-stratified alignments on 25 tumor and normal 10XGenomics scRNA-seq datasets (>200,000 cells). Our approach identified 24,528 exonic non-dbSNP single cell expressed (sce)SNVs, a third of which are shared across multiple samples. The novel sceSNVs include unreported somatic and germline variants, as well as RNA-originating variants; some are expressed in up to 17% of the cells, and many are found in known cancer genes. Our findings suggest that there is an unacknowledged repertoire of expressed genetic variants, possibly recurrent and common across samples, in the normal and cancer transcriptome.

A possible explanation for chronic inflammation in HIV-infected individuals treated with anti-retroviral therapy is hyperreactivity of myeloid cells due to a phenomenon called "trained immunity." Here, we demonstrate that human monocyte-derived macrophages originating from monocytes initially treated with extracellular vesicles containing HIV-1 protein Nef (exNef), but differentiating in the absence of exNef, release increased levels of pro-inflammatory cytokines after lipopolysaccharide stimulation. This effect is associated with chromatin changes at the genes involved in inflammation and cholesterol metabolism pathways and upregulation of the lipid rafts and is blocked by methyl-β-cyclodextrin, statin, and an inhibitor of the lipid raft-associated receptor IGF1R. Bone-marrow-derived macrophages from exNef-injected mice, as well as from mice transplanted with bone marrow from exNef-injected animals, produce elevated levels of tumor necrosis factor α (TNF-α) upon stimulation. These phenomena are consistent with exNef-induced trained immunity that may contribute to persistent inflammation and associated co-morbidities in HIV-infected individuals with undetectable HIV load.

Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.

BACKGROUND:Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell.RESULTS:Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci.CONCLUSION:ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset.AVAILABILITY:https://github.com/HorvathLab/NGS/tree/master/scReQTL.

Inhibition of the angiotensin type 1 receptor (AT 1 R) has been shown to decrease fear responses in both humans and rodents. These effects are attributed to modulation of extinction learning, however the contribution of AT 1 R to alternative memory processes remains unclear. Using classic Pavlovian conditioning combined with radiotelemetry and whole-genome RNA sequencing, we evaluated the effects of the AT 1 R antagonist losartan on fear memory reconsolidation. Following the retrieval of conditioned auditory fear memory, animals were given a single intraperitoneal injection of losartan or saline. In response to the conditioned stimulus (CS), losartan-treated animals exhibited significantly less freezing at 24 h and 1 week; an effect that was dependent upon memory reactivation and independent of conditioned cardiovascular reactivity. Using an unbiased whole-genome RNA sequencing approach, transcriptomic analysis of the basolateral amygdala (BLA) identified losartan-dependent differences in gene expression during the reconsolidation phase. These findings demonstrate that post-retrieval losartan modifies behavioral and transcriptomic markers of conditioned fear memory, supporting an important regulatory role for this receptor in reconsolidation and as a potential pharmacotherapeutic target for maladaptive fear disorders such as PTSD.

Motivation By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets. Results We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci. Availability and implementation A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. Supplementary information Supplementary data are available at Bioinformatics online.

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

SummaryRsQTL is a tool for identification of splicing quantitative trait loci (sQTLs) from RNA-sequencing (RNA-seq) data by correlating the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA) with the proportion of molecules spanning local exon-exon junctions at loci with differential intron excision (percent spliced in, PSI). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx). RsQTL does not require matched DNA and can identify a subset of expressed sQTL loci. Due to the dynamic nature of VAFRNA, RsQTL is applicable for the assessment of conditional and dynamic variation-splicing relationships.Availability and implementationhttps://github.com/HorvathLab/RsQTL.Contacthorvatha@gwu.edu or jsein@gwmail.gwu.eduSupplementary InformationRsQTL_Supplementary_Data.zip